MiMo V2.5 Pro Thinking
MiMo V2.5 Pro with Xiaomi thinking enabled for coding, long-context reasoning, and agentic orchestration.
- Reasoning
- Tool Calling
- Structured Output
Added Jun 3, 2026
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.43
- Output
- $0.87
- Cache read
- $0.0036
Specifications
- Context window
- 1M
- Max output
- 131.1K
- Parameters
- 1T / 42B
- Total / active
- Avg output (7d)
- 632 tokens
- Longer than 51% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
26.0
Coding Index
60.2
Agentic Index
21.3
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
13.7%
Better than 37% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
73.3%
Better than 20% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
881 Elo
Better than 40% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1107 Elo
Better than 55% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
4.0%
Better than 22% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
79.7%
Better than 85% of models compared
MLCR-AA
Medical long-context reasoning
9.4%
Better than 29% of models compared
Reasoning
HLE
Humanity's Last Exam
35.7%
Better than 84% of models compared
IFBench
Instruction-following benchmark
79.9%
Better than 98% of models compared
CritPt
Research-level physics reasoning
4.0%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
50.6%
Better than 57% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
22.4%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
24.7%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
86.6%
Better than 81% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
43.2%
Better than 90% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
94.2%
Better than 92% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
79.7%
Better than 85% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
30.4%
Last updated Oct 2, 2026
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